Object detection is an important research domain in computer vision. Object detection is the basis for further analyzing objects. Based on the object detection, object tracking and behavioral analysis can be implemented. Currently, there are a number of object detection models known in the academic field. A deformable part model (DPM) may be the most popular model for object detection in images in the past two years. Because it can accurately detect objects, DPM has become popular and is recognized as a desired object detection algorithm.
DPM includes three components: (1) a coarse global root template (also called a root filter) that approximately covers an entire object; (2) several high resolution part templates (also called part filters) that cover smaller parts of the object; and (3) space locations of the part templates relative to the global root template.
The feature map is an array whose entries are d-dimensional feature vectors computed from a dense grid of locations in an image. A filter is a rectangular template defined by an array of d-dimensional weight vectors. The score of a filter F at a position (x; y) in a feature map G is the “dot product” of the filter and a subwindow of the feature map with top-left corner at (x; y).
According to a score of a detection window, whether there is an object to be detected is determined. The score of the detection window is the sum of the score of the root filter and the scores of all part filters. The score of each part filter is the maximum value of the scores of various spatial locations of the part filter. The scores of the spatial locations of each part filter are obtained by the scores of each part filter at their respective locations minus a deformation cost that depends on the relative location of each part with respect to the root.
Objectness measure can detect regions that possibly contain the object, improving the accuracy of the object detection. A desired objectness measure can accelerate the speed of the object detection. The desired objectness measure needs to meet the following requirements: a higher detection rate; suspected object regions remain as few as possible; higher computational efficiency; and scalability.
As described above, although existing DPM object detection methods can detect accurately the object to be detected, the process is time-consuming. Therefore, it is very difficult to directly apply DPM to real products. Objectness measure with a higher detection rate and scalability can detect the object in real time. However, because a learning method used in the objectness measure is relatively simple, more regions may be generated. Therefore, it is necessary to provide a new object detection method. That is, in a new object detection method, at the beginning, the objectness measure may be applied; and then a fast Fourier object detection method based on DPM (ODDPM) may be applied to the regions that possibly contain the object.
The disclosed methods and systems are directed to solve one or more problems set forth above and other problems. For example, the fast object detection method based on DPM can be applied in battlefield surveillance, video monitoring, image compression, image retrieve, human-computer interaction, and so on.